Golf is a 700 year old sport, popular worldwide. A variety of golf training techniques have been developed over these years. Modern golf training aids utilize computer technologies, video tracking and virtual reality. As the body movements during a golf swing appear to be so complex, golfers always intuitively assumed that if they were able to stabilize the most important moves, their swing would improve. However, since these moves were difficult to track and control, the only criteria for a good golf swing was the resulting trajectory of a golf ball.
Electromyography (EMG) studies of golf have examined activity patterns in the arm, trunk and leg muscles during the execution of strokes (Marta et al. 2012). The majority of these studies were conducted in professional golfers. However, it always remained a mystery of what exactly had to be improved in a golf swing.
The present invention describes the EMG pattern that was discovered in players of different gender, age, and skill level. Surface EMG recordings were employed from intrinsic hand muscles to evaluate the characteristics of the golf grip during the execution of the swing sequence (FIG. 1). Although it is well known that EMG signals are generated during physical activity, these signals were not utilized for the improvement of athletic performance, e.g. a golf swing. Intrinsic muscles of hands can generate a common pattern of EMG during a golf swing. This pattern should be generated, if all elements of the golf swing are performed correctly by a golf player. Indeed, the point of contact of the hands with the club serves as the end effectors during golf strokes. All forces are applied to the golf club through the hands, which makes intrinsic hand muscles very special in this motor task even though many muscles of the body generate the swing. By squaring EMG values of selected hand muscles in both hands over time intervals, a well defined pattern maybe exhibited during the execution of golf swing.
The present invention provides the inside view on important muscle movement in a golf swing, by analyzing the electrical activity of both hands muscles and evaluating movement based on this information. It simply tells a player, which move(s) were wrong. Having this information, the player has to repeat the whole swing again paying close attention to the moves that were marked as wrong, or missing.
Every serious golf player intuitively knows that the outside views of golf swings are incomplete. There is infinite number of ways players can contract or relax their muscles during the movements in a golf swing, even when they are just standing still. No camera would be able to detect their muscle activity. Yet, this type of activity can make a difference in a swing.
The present invention adds very important information to all existing learning methods that are only based on the outside observation. The electrical activity of hand muscles during a golf swing are not sporadic and not even unique to golf players, genders, or age groups. Identified herein is a common pattern of EMG intensities that occurs during a successful golf swing. The successful golf swing is defined as a sequence of controlled moves, causing a ball to end up in a desired location. Different athletes usually place emphasis on using different muscles of the body, while performing a complex move such as a golf swing, chip, putt, drive, etc. The intuitive choice of their style is based on the constitution and development of their body. However, a common pattern of muscle activity of both hands during the golf swing was discovered. In this document, a method of identifying: 1) this pattern, 2) when it is generated and 3) how to translate it into the sequence of hand moves is described. The successful pattern of electrical activity of two muscles in each hand is defined. The appearance of this pattern in the muscles of both hands is a criterion of successfully controlled movements.
The sequence of moves is controlled, if it is possible to find the correspondent move of an athlete for every peak in muscle activity. For example, assuming the sequence of moves as shown in FIG. 1, in order for the moves to be successfully controlled, the system should be able to identify at least four moves from the muscle activity. When professionals successfully swing a golf club, the electrical activity of their hand muscles is very similar and clearly indicates which moves resulted from it. The present invention can extract this information and map it to individual moves according to their position in a sequence of a golf swing. The present invention will help golf players to eliminate their mistakes which cannot be observed from outside.
EMG pattern is not the precise measure of muscles activity, but an approximation. There is some variance of the activity within a pattern. Some combinations of this activity will result in a successful swing. It is a necessary condition for a player to generate the EMG pattern, but not the sufficient condition for a successful swing. For example, even if the EMG pattern was generated, a player can miss the ball. This will not result in a successful swing. Another scenario is, when a player holds the club the wrong way, or under the wrong angle. This swing will not be successful, even if the correct EMG pattern will be generated.
This invention is based on the discovery made by Michael Linderman. Linderman discovered how the fixation of specific movements can generate almost non variant pattern activity in alpha motor neurons during a successful golf swing and the algorithm for translating the EMG peaks into the correspondent hand moves. He also observed the difference with non professional golf students, and inexperienced athletes.
U.S. Pat. No. 9,192,335 discloses a system and method for “Athletic Glove Providing Feedback Regarding Grip to a Wearer”. In that patent, a user had differential amplifiers on both hands. Two pairs of differential electrodes were connected to two muscle groups on each hand. Amplified signals were digitized at 1000 Hz sampling rate. Then it was possible to identify the Electromyography (EMG) peaks according to the golf swing sequence shown on FIG. 1. This identification is based on knowing the time of the swing (a user will tap on a screen or otherwise indicate the beginning of a swing) and on synchronization of all four muscle's EMG peaks using the largest peak correspondent to the impact move, i.e. when a golfer is hitting the ball. However, it was not obvious even from the previous disclosure how to make the motor neurons generating the same activity in hand muscles during, for example, such complex athletic movements as in golf, or baseball swing.